Journal of Water and Wastewater Science and Engineering

Journal of Water and Wastewater Science and Engineering

Daily Urban Water Consumption Prediction and Optimization of Pumping Station Operation Hours: A Case Study of Najaf Abad

Document Type : Research Paper

Authors
Pedram Jazayeri 1
Hamidreza Safavi 2
Mohamadrez Nazemizadeh 3
Mohamad Saleh Ebrahimi 4
Alireza Rahmatpanah 5
1 Ph.D. in Water Resources Management and Engineering, Faculty of Civil and Transportation, Isfahan University, Isfahan, Iran.
2 Professor, Faculty of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.
3 Director of Control Systems and Energy Office, Water and Wastewater Company of Isfahan Province, Isfahan, Iran.
4 Telemetry Specialist, Water and Wastewater Company of Isfahan Province, Isfahan, Iran.
5 Head of the Building Design and Renovation Group, Water and Wastewater Company of Isfahan Province, Isfahan, Iran.
10.22112/jwwse.2024.411391.1373
Abstract
In recent years, engineers and operators have shown a greater interest in employing optimization method and making networks smart over other costlier and time-consuming approaches such as network rehabilitation, asset management, and network equipment upgrading. In the present study, the daily water consumption in the urban water distribution network is predicted based on four input features: day of the year, day of the week, continuity of holidays, and maximum daily air temperature, using moving average methods, linear regression, some artificial intelligence methods including multilayer perceptron neural network, and radial basis function neural network. Subsequently, based on the predicted values, the optimal scheduling of pump station activation hours is determined considering the hourly consumption pattern and the water levels in the upstream and downstream reservoirs of the pumping station. This scheduling aims to reduce the electricity cost of the pumping station with the fixed speed pumps based on different electricity tariffs. The method has been applied to the Najaf Abad urban water network, resulting in a reduction of 1.2% to 13.3% in the electricity cost of the pumping station compared to the traditional operational mode due to the different time interval parameter values for pumping.
Keywords
Multi-Layer Perceptron
Radial Basis Function
Mixed Integer Linear Programming
Water Network
Pump Station
بابایی، ن.، تابش، م.، و نظیف، س.، (1396)، "بهینه‌سازی کیفیت آب در شبکه‌های توزیع بر اساس وضعیت دور پمپ، نحوه تزریق کلر و نوع روش تحلیل "، مجله آب و فاضلاب، 28(2)، 55-64، https://doi.org./10.22093/wwj.2017.16401.
دینی، م.، همتی، م.، و هاشمی، س.، (1400)، "بیشینه‌سازی کارایی هیدرولیکی شبکه توزیع آب شهر خمام با برنامه‌ریزی بهینه تعداد و سرعت پمپ‌ها"، مجله آب و فاضلاب، 32(6)، 36-47، https://doi.org./10.22093/wwj.2021.275013.3118.
شکفته، م.ر.، جلیلی قاضی‎زاده، م.ر.، و یزدی، ج.، (1399)، "تئوری شناسایی محدوده‌ی نشت در نواحی مجزای مجازی شبکه‌های توزیع آب با استفاده از شبکه‌ی عصبی مصنوعی"، تحقیقات منابع آب ایران، 16(3)، 47-62، https://www.iwrr.ir/article_108525.html?lang=fa.
کرمی، ج.، مقدم، ع.، فرید حسینی، ع.، ثنایی نژاد، ح.، و ضیایی، ع.ن.، (1396)، " بهینه‌سازی مصرف انرژی در ایستگاه‌های پمپاژ با استفاده از ابزار Darwin Scheduler"، علوم و مهندسی آب و فاضلاب، 2(1)، 3-12، https://doi.org./10.22112/JWWSE.2017.87910.1006.
مطیعی، ه.، و قاسم‎نژاد، س.، (1396)، "کاربرد وتوسعه مدل‎های رگرسیونی برای پیش‎بینی میزان شکست لوله‎های شبکه توزیع آب شهری، مورد مطالعاتی ناحیه یک منطقه یک تهران"، علوم و مهندسی آب و فاضلاب، 2(2)، 48-58، https://doi.org./10.22112/jwwse.2017.89374.1024.
نصراللهی، ح.، صفایی بروجنی، ر.، و صالح، س.م.ح.، (1400)، "بهینه‌سازی فشار-انرژی در شبکه توزیع آب (مطالعه موردی: شهر بهارستان اصفهان)"، علوم و مهندسی آب و فاضلاب، 6(4)، 32-44، https://doi.org./10.22112/jwwse.2021.277877.1264..
Adamowski, J., and Karapataki, C., (2010), “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms”, Journal of Hydrologic Engineering, 15(10), 729-743, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000245.
Alharsha, I., Memon, F.A., Farmani, R., and Hussien, W.E.A., )2022(, “An investigation of domestic water consumption in Sirte, Libya”, Urban Water Journal, 19(9), 922-944, https://doi.org/10.1080/1573062X.2022.2105239.
Basnet, L., Brill, D., Ranjithan, R., and Mahinthakumar, K., )2023(, “Supervised Machine Learning approaches for leak localization in water distribution systems: Impact of complexities of leak characteristics”, Journal of Water Resources Planning and Management, 149(8), 04023032, https://doi.org/10.1061/JWRMD5.WRENG-6047.
Buhmann, M.D., (2000), Radial basis functions, Acta Numerica, Cambridge University Press, 9, 1-38, https://doi.org/10.1017/S0962492900000015.
Fernández García, I., Ferras, D., and Mc Nabola, A., (2019), “Potential of energy recovery and water saving using micro-hydropower in rural water distribution networks”, Journal of Water Resources Planning and Management, 145(3), 05019001, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001045.
Gardner, M.W., and Dorling, S.R., (1998), “Artificial neural networks (the multilayer perceptron), A review of applications in the atmospheric sciences”, Atmospheric Environment, 32(14-15), 2627-2636, https://doi.org/10.1016/S1352-2310(97)00447-0.
Hajgató, G., Paál, G., and Gyires-Tóth, B., )2020(, “Deep reinforcement learning for real-time optimization of pumps in water distribution systems”, Journal of Water Resources Planning and Management, 146(11), 04020079, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001287.
Hansun, S., (2013), “A new approach of moving average method in time series analysis”, In: 2013 Conference on New Media Studies (CoNMedia), IEEE, 1-4, https://doi.org/10.1109/CoNMedia.2013.6708545.
Pandey, P., Bokde, N.D., Dongre, S., and Gupta, R., )2021(, “Hybrid models for water demand forecasting”, Journal of Water Resources Planning and Management, 147(2), 04020106, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001331.
Rapp, A.H., Capener, A.M., and Sowby, R.B., (2023), “Adoption of Artificial Intelligence in drinking water operations: A survey of progress in the United States”, Journal of Water Resources Planning and Management, 149(7), 06023002, https://doi.org/10.1061/JWRMD5.WRENG-5870.
Smola, A.J., and Schölkopf, B., (2004), “A tutorial on support vector regression”, Statistics and Computing, 14, 199-222, https://doi.org/10.1023/B:STCO.0000035301.49549.88.
Topalli, A.K., Erkmen, I., and Topalli, I., (2006), “Intelligent short-term load forecasting in Turkey”, International Journal of Electrical Power and Energy Systems, 28(7), 437-447, https://doi.org/10.1016/j.ijepes.2006.02.004.
Journal of Water and Wastewater Science and Engineering
Volume 9, Issue 3
Autumn 2024
Pages 17-28

  • Receive Date 13 August 2023
  • Revise Date 13 January 2024
  • Accept Date 15 January 2024